How to Leverage Data Better for Value-Based Payment Success

Transitioning to value-based payment (VBP) contracts is pivotal for shifting from fee-for-service and volume-based care to proactive, data-driven care that improves, enhances patent experiences and reduces unnecessary spending, but the journey is fraught with complexity for both payers and providers. Fragmented, non-standard, delayed, and siloed data sources create significant barriers to building the single source of truth that is necessary to drive improved outcomes and operational efficiency.

As payers and providers try to move away from fee-for-service toward models that reward value, the ability to collect, integrate, and act on timely, accurate data becomes the linchpin of success. Here, we examine three common pitfalls faced by payers and providers on their VBP journey and share practical solutions to enable successful VBP contracts.

Overcoming System Fragmentation

It’s easy to focus on the technical elements of a VBP data foundation, such as warehouses, feeds, and analytics dashboards. However, the most common obstacles are often less visible. For payers, data latency is one of the most overlooked challenges. Delayed or infrequent data flows can hinder rapid program configuration and prevent organizations from responding quickly to emerging trends. Providers, meanwhile, often rush to build analytics dashboards without first addressing data quality and governance. Scattered patient data across EHRs, billing, labs, and pharmacy systems leads to duplication, gaps, and inconsistencies.

One of the biggest barriers to VBP success is system fragmentation. Healthcare organizations can have five, ten, or even more systems with unique data structures and coding conventions. Even when technical interoperability is achieved, semantic differences—such as varying definitions of risk scores or diagnosis codes—can lead to confusion and errors. Data quality issues are often compounded when patient information is spread across multiple platforms, resulting in mismatched demographics, inconsistent lab result formats, and incomplete medication lists.

Forward-thinking organizations address these challenges with a multi-pronged approach: eliminating “black boxes” by ensuring data flows transparently between systems, standardizing performance reporting, and adopting industry frameworks like the HL7 Da Vinci Project and FHIR-based implementation guides. Establishing a dedicated data governance committee that includes clinical, compliance, and IT stakeholders is vital. Data normalization layers, acting as translators between legacy systems, also help ensure that data is consistent and reliable at the source, reducing manual reconciliation and supporting accurate risk modeling.

Incorporating Social Determinants of Health

Social determinants of health (SDOH) are a critical driver of health, accounting for 80% or more of outcomes. Yet many VBP models still focus narrowly on clinical metrics, missing the opportunity to address factors like food insecurity, housing, and transportation. Payers have collected SDOH data for years, but their efforts are often limited to a small fraction of the population, and available data sets can be incomplete or inaccurate.

To truly move the needle, SDOH data collection must be embedded into routine clinical workflows, not treated as an afterthought or a separate form. Making SDOH screening part of every patient interaction helps ensure that the care team includes the right resources (e.g., social workers) for the specific needs of the patient. For example, automating referrals—so that a positive food insecurity screen triggers an immediate connection to community resources—closes the loop and drives better outcomes. Smart, risk-adjusted VBP contracts are now adjusting expected costs to account for SDOH factors, helping ensure that providers aren’t penalized for serving higher-need populations and that interventions are appropriately resourced.

Integrating Artificial Intelligence Responsibly

There’s little question that artificial intelligence (AI) is reshaping the VBP landscape. AI and machine learning algorithms can identify emerging high-risk patients earlier, enabling early targeted interventions that reduce avoidable hospitalizations and improve care quality. However, adoption remains uneven: given the regulatory scrutiny surrounding using AI with protected health information and the need for transparency, many payers are still hesitant to leverage the technology. The federal government is moving toward stronger governance, emphasizing that transparency in AI-driven results is not optional.

On the provider side, generative AI is poised to move beyond prediction to orchestrating care itself: synthesizing patient data, generating care plans, coordinating with partners, and automating education and reporting. The key to realizing AI’s full potential will be building trust, ensuring clear governance, and making AI-powered tools accessible and actionable for care teams. As organizations learn to harness the flood of available data, the future of VBP will belong not to those with the most data, but to those who use it most intelligently and collaboratively.

Embracing the Data Journey

VBP is a journey, and building a strong data foundation is both the greatest challenge and opportunity. By overcoming fragmentation, prioritizing SDOH, and embracing trustworthy AI, payers and providers can build a data engine that powers better health for their members and patients.

For a deeper dive, watch our new on-demand webinar as we discuss how to achieve a strong data foundation for your VBP programs.